Learning internal ranges from network traffic data to augment anomaly detection systems

ABSTRACT

In one embodiment, a device in a network receives traffic records indicative of network traffic between different sets of host address pairs. The device identifies one or more address grouping constraints for the sets of host address pairs. The device determines address groups for the host addresses in the sets of host address pairs based on the one or more address grouping constraints. The device provides an indication of the address groups to an anomaly detector.

RELATED APPLICATION

The application is a continuation application of U.S. patent applicationSer. No. 15/263,487, filed on Sep. 13, 2016, entitled LEARNING INTERNALRANGES FROM NETWORK TRAFFIC DATA TO AUGMENT ANOMALY DETECTION SYSTEMS,by Laurent Sartran, et al., the contents of which are incorporatedherein by reference.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to learning internal address ranges from network trafficdata to augment anomaly detection systems.

BACKGROUND

Generally, Internet Behavioral Analytics (IBA) refers to the use ofadvanced analytics coupled with various networking technologies, todetect anomalies in a network. Such anomalies may include, for example,network attacks, malware, misbehaving and misconfigured devices, and thelike. For example, the ability to model the behavior of a device (e.g.,a host, networking switch, router, etc.) allows for the detection ofmalware, which is complimentary to the use of firewalls that use staticsignature. Observing behavioral changes (e.g., deviation from modeledbehavior) using flows records, deep packet inspection, and the like,allows for the detection of an anomaly such as a horizontal movement(e.g. propagation of a malware, . . . ) or an attempt to performinformation exfiltration, prompting the system to take remediationactions automatically.

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests, to prevent legitimaterequests from being processed. A DoS attack may also be distributed, toconceal the presence of the attack. For example, a distributed DoS(DDoS) attack may involve multiple attackers sending malicious requests,making it more difficult to distinguish when an attack is underway. Whenviewed in isolation, a particular one of such a request may not appearto be malicious. However, in the aggregate, the requests may overload aresource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example self learning network (SLN)infrastructure;

FIG. 4 illustrates an example distributed learning agent (DLA);

FIG. 5 illustrates an example of network traffic being conveyed;

FIG. 6 illustrates an example address grouping process;

FIGS. 7A-7C illustrate examples of the determination of address groups;and

FIG. 8 illustrates an example simplified procedure for providing addressgroups to an anomaly detector.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device in anetwork receives traffic records indicative of network traffic betweendifferent sets of host address pairs. The device identifies one or moreaddress grouping constraints for the sets of host address pairs. Thedevice determines address groups for the host addresses in the sets ofhost address pairs based on the one or more address groupingconstraints. The device provides an indication of the address groups toan anomaly detector.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise routing process244 (e.g., routing services) and illustratively, a self learning network(SLN) process 248, as described herein, any of which may alternativelybe located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Routing process/services 244 include computer executable instructionsexecuted by processor 220 to perform functions provided by one or morerouting protocols, such as the Interior Gateway Protocol (IGP) (e.g.,Open Shortest Path First, “OSPF,” andIntermediate-System-to-Intermediate-System, “IS-IS”), the Border GatewayProtocol (BGP), etc., as will be understood by those skilled in the art.These functions may be configured to manage a forwarding informationdatabase including, e.g., data used to make forwarding decisions. Inparticular, changes in the network topology may be communicated amongrouters 200 using routing protocols, such as the conventional OSPF andIS-IS link-state protocols (e.g., to “converge” to an identical view ofthe network topology).

Notably, routing process 244 may also perform functions related tovirtual routing protocols, such as maintaining VRF instance, ortunneling protocols, such as for MPLS, generalized MPLS (GMPLS), etc.,each as will be understood by those skilled in the art. Also, EVPN,e.g., as described in the IETF Internet Draft entitled “BGP MPLS BasedEthernet VPN”<draft-ietf-12vpn-evpn>, introduce a solution formultipoint L2VPN services, with advanced multi-homing capabilities,using BGP for distributing customer/client media access control (MAC)address reach-ability information over the core MPLS/IP network.

SLN process 248 includes computer executable instructions that, whenexecuted by processor(s) 220, cause device 200 to perform anomalydetection functions as part of an anomaly detection infrastructurewithin the network. In general, anomaly detection attempts to identifypatterns that do not conform to an expected behavior. For example, inone embodiment, the anomaly detection infrastructure of the network maybe operable to detect network attacks (e.g., DDoS attacks, the use ofmalware such as viruses, rootkits, etc.). However, anomaly detection inthe context of computer networking typically presents a number ofchallenges: 1.) a lack of a ground truth (e.g., examples of normal vs.abnormal network behavior), 2.) being able to define a “normal” regionin a highly dimensional space can be challenging, 3.) the dynamic natureof the problem due to changing network behaviors/anomalies, 4.)malicious behaviors such as malware, viruses, rootkits, etc. may adaptin order to appear “normal,” and 5.) differentiating between noise andrelevant anomalies is not necessarily possible from a statisticalstandpoint, but typically also requires domain knowledge.

Anomalies may also take a number of forms in a computer network: 1.)point anomalies (e.g., a specific data point is abnormal compared toother data points), 2.) contextual anomalies (e.g., a data point isabnormal in a specific context but not when taken individually), or 3.)collective anomalies (e.g., a collection of data points is abnormal withregards to an entire set of data points). Generally, anomaly detectionrefers to the ability to detect an anomaly that could be triggered bythe presence of malware attempting to access data (e.g., dataexfiltration), spyware, ransom-ware, etc. and/or non-malicious anomaliessuch as misconfigurations or misbehaving code. Particularly, an anomalymay be raised in a number of circumstances:

-   -   Security threats: the presence of a malware using unknown        attacks patterns (e.g., no static signatures) may lead to        modifying the behavior of a host in terms of traffic patterns,        graphs structure, etc. Machine learning processes may detect        these types of anomalies using advanced approaches capable of        modeling subtle changes or correlation between changes (e.g.,        unexpected behavior) in a highly dimensional space. Such        anomalies are raised in order to detect, e.g., the presence of a        0-day malware, malware used to perform data ex-filtration thanks        to a Command and Control (C2) channel, or even to trigger        (Distributed) Denial of Service (DoS) such as DNS reflection,        UDP flood, HTTP recursive get, etc. In the case of a (D)DoS,        although technical an anomaly, the term “DoS” is usually used.        SLN process 248 may detect malware based on the corresponding        impact on traffic, host models, graph-based analysis, etc., when        the malware attempts to connect to a C2 channel, attempts to        move laterally, or exfiltrate information using various        techniques.    -   Misbehaving devices: a device such as a laptop, a server of a        network device (e.g., storage, router, switch, printer, etc.)        may misbehave in a network for a number of reasons: 1.) a user        using a discovery tool that performs (massive) undesirable        scanning in the network (in contrast with a lawful scanning by a        network management tool performing device discovery), 2.) a        software defect (e.g. a switch or router dropping packet because        of a corrupted RIB/FIB or the presence of a persistent loop by a        routing protocol hitting a corner case).    -   Dramatic behavior change: the introduction of a new networking        or end-device configuration, or even the introduction of a new        application may lead to dramatic behavioral changes. Although        technically not anomalous, an SLN-enabled node having computed        behavioral model(s) may raise an anomaly when detecting a brutal        behavior change. Note that in such as case, although an anomaly        may be raised, a learning system such as SLN is expected to        learn the new behavior and dynamically adapts according to        potential user feedback.    -   Misconfigured devices: a configuration change may trigger an        anomaly: a misconfigured access control list (ACL), route        redistribution policy, routing policy, QoS policy maps, or the        like, may have dramatic consequences such a traffic black-hole,        QoS degradation, etc. SLN process 248 may advantageously        identify these forms of misconfigurations, in order to be        detected and fixed.

In various embodiments, SLN process 248 may utilize machine learningtechniques, to perform anomaly detection in the network. In general,machine learning is concerned with the design and the development oftechniques that take as input empirical data (such as network statisticsand performance indicators), and recognize complex patterns in thesedata. One very common pattern among machine learning techniques is theuse of an underlying model M, whose parameters are optimized forminimizing the cost function associated to M, given the input data. Forinstance, in the context of classification, the model M may be astraight line that separates the data into two classes (e.g., labels)such that M=a*x+b*y+c and the cost function would be the number ofmisclassified points. The learning process then operates by adjustingthe parameters a,b,c such that the number of misclassified points isminimal. After this optimization phase (or learning phase), the model Mcan be used very easily to classify new data points. Often, M is astatistical model, and the cost function is inversely proportional tothe likelihood of M, given the input data.

Computational entities that rely on one or more machine learningtechniques to perform a task for which they have not been explicitlyprogrammed to perform are typically referred to as learning machines. Inparticular, learning machines are capable of adjusting their behavior totheir environment. For example, a learning machine may dynamically makefuture predictions based on current or prior network measurements, maymake control decisions based on the effects of prior control commands,etc.

For purposes of anomaly detection in a network, a learning machine mayconstruct a model of normal network behavior, to detect data points thatdeviate from this model. For example, a given model (e.g., a supervised,un-supervised, or semi-supervised model) may be used to generate andreport anomaly scores to another device. Example machine learningtechniques that may be used to construct and analyze such a model mayinclude, but are not limited to, nearest neighbor (NN) techniques (e.g.,k-NN models, replicator NN models, etc.), statistical techniques (e.g.,Bayesian networks, etc.), clustering techniques (e.g., k-means, etc.),neural networks (e.g., reservoir networks, artificial neural networks,etc.), support vector machines (SVMs), or the like.

One class of machine learning techniques that is of particular use inthe context of anomaly detection is clustering. Generally speaking,clustering is a family of techniques that seek to group data accordingto some typically predefined notion of similarity. For instance,clustering is a very popular technique used in recommender systems forgrouping objects that are similar in terms of people's taste (e.g.,because you watched X, you may be interested in Y, etc.). Typicalclustering algorithms are k-means, density based spatial clustering ofapplications with noise (DBSCAN) and mean-shift, where a distance to acluster is computed with the hope of reflecting a degree of anomaly(e.g., using a Euclidian distance and a cluster based local outlierfactor that takes into account the cluster density).

Replicator techniques may also be used for purposes of anomalydetection. Such techniques generally attempt to replicate an input in anunsupervised manner by projecting the data into a smaller space (e.g.,compressing the space, thus performing some dimensionality reduction)and then reconstructing the original input, with the objective ofkeeping the “normal” pattern in the low dimensional space. Exampletechniques that fall into this category include principal componentanalysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP)ANNs (e.g., for non-linear models), and replicating reservoir networks(e.g., for non-linear models, typically for time series).

According to various embodiments, SLN process 248 may also usegraph-based models for purposes of anomaly detection. Generallyspeaking, a graph-based model attempts to represent the relationshipsbetween different entities as a graph of nodes interconnected by edges.For example, ego-centric graphs have been used to represent therelationship between a particular social networking profile and theother profiles connected to it (e.g., the connected “friends” of a user,etc.). The patterns of these connections can then be analyzed forpurposes of anomaly detection. For example, in the social networkingcontext, it may be considered anomalous for the connections of aparticular profile not to share connections, as well. In other words, aperson's social connections are typically also interconnected. If nosuch interconnections exist, this may be deemed anomalous.

An example self learning network (SLN) infrastructure that may be usedto detect network anomalies is shown in FIG. 3, according to variousembodiments. Generally, network devices may be configured to operate aspart of an SLN infrastructure to detect, analyze, and/or mitigatenetwork anomalies such as network attacks (e.g., by executing SLNprocess 248). Such an infrastructure may include certain network devicesacting as distributed learning agents (DLAs) and one or moresupervisory/centralized devices acting as a supervisory and controlagent (SCA). A DLA may be operable to monitor network conditions (e.g.,router states, traffic flows, etc.), perform anomaly detection on themonitored data using one or more machine learning models, reportdetected anomalies to the SCA, and/or perform local mitigation actions.Similarly, an SCA may be operable to coordinate the deployment andconfiguration of the DLAs (e.g., by downloading software upgrades to aDLA, etc.), receive information from the DLAs (e.g., detectedanomalies/attacks, compressed data for visualization, etc.), provideinformation regarding a detected anomaly to a user interface (e.g., byproviding a webpage to a display, etc.), and/or analyze data regarding adetected anomaly using more CPU intensive machine learning processes.

One type of network attack that is of particular concern in the contextof computer networks is a Denial of Service (DoS) attack. In general,the goal of a DoS attack is to prevent legitimate use of the servicesavailable on the network. For example, a DoS jamming attack mayartificially introduce interference into the network, thereby causingcollisions with legitimate traffic and preventing message decoding. Inanother example, a DoS attack may attempt to overwhelm the network'sresources by flooding the network with requests (e.g., SYN flooding,sending an overwhelming number of requests to an HTTP server, etc.), toprevent legitimate requests from being processed. A DoS attack may alsobe distributed, to conceal the presence of the attack. For example, adistributed DoS (DDoS) attack may involve multiple attackers sendingmalicious requests, making it more difficult to distinguish when anattack is underway. When viewed in isolation, a particular one of such arequest may not appear to be malicious. However, in the aggregate, therequests may overload a resource, thereby impacting legitimate requestssent to the resource.

Botnets represent one way in which a DDoS attack may be launched againsta network. In a botnet, a subset of the network devices may be infectedwith malicious software, thereby allowing the devices in the botnet tobe controlled by a single master. Using this control, the master canthen coordinate the attack against a given network resource.

DoS attacks are relatively easy to detect when they are brute-force(e.g. volumetric), but, especially when highly distributed, they may bedifficult to distinguish from a flash-crowd (e.g., an overload of thesystem due to many legitimate users accessing it at the same time). Thisfact, in conjunction with the increasing complexity of performedattacks, makes the use of “classic” (usually threshold-based) techniquesuseless for detecting them. However, machine learning techniques maystill be able to detect such attacks, before the network or servicebecomes unavailable. For example, some machine learning approaches mayanalyze changes in the overall statistical behavior of the networktraffic (e.g., the traffic distribution among flow flattens when a DDoSattack based on a number of microflows happens). Other approaches mayattempt to statistically characterizing the normal behaviors of networkflows or TCP connections, in order to detect significant deviations.Classification approaches try to extract features of network flows andtraffic that are characteristic of normal traffic or malicious traffic,constructing from these features a classifier that is able todifferentiate between the two classes (normal and malicious).

As shown in FIG. 3, routers CE-2 and CE-3 may be configured as DLAs andserver 152 may be configured as an SCA, in one implementation. In such acase, routers CE-2 and CE-3 may monitor traffic flows, router states(e.g., queues, routing tables, etc.), or any other conditions that maybe indicative of an anomaly in network 100. As would be appreciated, anynumber of different types of network devices may be configured as a DLA(e.g., routers, switches, servers, blades, etc.) or as an SCA.

Assume, for purposes of illustration, that CE-2 acts as a DLA thatmonitors traffic flows associated with the devices of local network 160(e.g., by comparing the monitored conditions to one or moremachine-learning models). For example, assume that device/node 10 sendsa particular traffic flow 302 to server 154 (e.g., an applicationserver, etc.). In such a case, router CE-2 may monitor the packets oftraffic flow 302 and, based on its local anomaly detection mechanism,determine that traffic flow 302 is anomalous. Anomalous traffic flowsmay be incoming, outgoing, or internal to a local network serviced by aDLA, in various cases.

In some cases, traffic 302 may be associated with a particularapplication supported by network 100. Such applications may include, butare not limited to, automation applications, control applications, voiceapplications, video applications, alert/notification applications (e.g.,monitoring applications), communication applications, and the like. Forexample, traffic 302 may be email traffic, HTTP traffic, trafficassociated with an enterprise resource planning (ERP) application, etc.

In various embodiments, the anomaly detection mechanisms in network 100may use Internet Behavioral Analytics (IBA). In general, IBA refers tothe use of advanced analytics coupled with networking technologies, todetect anomalies in the network. Although described later with greaterdetails, the ability to model the behavior of a device (networkingswitch/router, host, etc.) will allow for the detection of malware,which is complementary to the use of a firewall that uses staticsignatures. Observing behavioral changes (e.g., a deviation from modeledbehavior) thanks to aggregated flows records, deep packet inspection,etc., may allow detection of an anomaly such as an horizontal movement(e.g. propagation of a malware, etc.), or an attempt to performinformation exfiltration.

FIG. 4 illustrates an example distributed learning agent (DLA) 400 ingreater detail, according to various embodiments. Generally, a DLA maycomprise a series of modules hosting sophisticated tasks (e.g., as partof an overall SLN process 248). Generally, DLA 400 may communicate withan SCA (e.g., via one or more northbound APIs 402) and any number ofnodes/devices in the portion of the network associated with DLA 400(e.g., via APIs 420, etc.).

In some embodiments, DLA 400 may execute a Network Sensing Component(NSC) 416 that is a passive sensing construct used to collect a varietyof traffic record inputs 426 from monitoring mechanisms deployed to thenetwork nodes. For example, traffic record inputs 426 may include Cisco™Netflow records, application identification information from a Cisco™Network Based Application Recognition (NBAR) process or anotherapplication-recognition mechanism, administrative information from anadministrative reporting tool (ART), local network state informationservice sets, media metrics, or the like.

Furthermore, NSC 416 may be configured to dynamically employ Deep PacketInspection (DPI), to enrich the mathematical models computed by DLA 400,a critical source of information to detect a number of anomalies. Alsoof note is that accessing control/data plane data may be of utmostimportance, to detect a number of advanced threats such as dataexfiltration. NSC 416 may be configured to perform data analysis anddata enhancement (e.g., the addition of valuable information to the rawdata through correlation of different information sources). Moreover,NSC 416 may compute various networking based metrics relevant for theDistributed Learning Component (DLC) 408, such as a large number ofstatistics, some of which may not be directly interpretable by a human.

In some embodiments, DLA 400 may also include DLC 408 that may perform anumber of key operations such as any or all of the following:computation of Self Organizing Learning Topologies (SOLT), computationof “features” (e.g., feature vectors), advanced machine learningprocesses, etc., which DLA 400 may use in combination to perform aspecific set of tasks. In some cases, DLC 408 may include areinforcement learning (RL) engine 412 that uses reinforcement learningto detect anomalies or otherwise assess the operating conditions of thenetwork. Accordingly, RL engine 412 may maintain and/or use any numberof communication models 410 that model, e.g., various flows of trafficin the network. In further embodiments, DLC 408 may use any other formof machine learning techniques, such as those described previously(e.g., supervised or unsupervised techniques, etc.). For example, in thecontext of SLN for security, DLC 408 may perform modeling of traffic andapplications in the area of the network associated with DLA 400. DLC 408can then use the resulting models 410 to detect graph-based and otherforms of anomalies (e.g., by comparing the models with current networkcharacteristics, such as traffic patterns. The SCA may also send updates414 to DLC 408 to update model(s) 410 and/or RL engine 412 (e.g., basedon information from other deployed DLAs, input from a user, etc.).

When present, RL engine 412 may enable a feed-back loop between thesystem and the end user, to automatically adapt the system decisions tothe expectations of the user and raise anomalies that are of interest tothe user (e.g., as received via a user interface of the SCA). In oneembodiment, RL engine 412 may receive a signal from the user in the formof a numerical reward that represents for example the level of interestof the user related to a previously raised event. Consequently the agentmay adapt its actions (e.g. search for new anomalies), to maximize itsreward over time, thus adapting the system to the expectations of theuser. More specifically, the user may optionally provide feedback thanksto a lightweight mechanism (e.g., ‘like’ or ‘dislike’) via the userinterface.

In some cases, DLA 400 may include a threat intelligence processor (TIP)404 that processes anomaly characteristics so as to further assess therelevancy of the anomaly (e.g. the applications involved in the anomaly,location, scores/degree of anomaly for a given model, nature of theflows, or the like). TIP 404 may also generate or otherwise leverage amachine learning-based model that computes a relevance index. Such amodel may be used across the network to select/prioritize anomaliesaccording to the relevancies.

DLA 400 may also execute a Predictive Control Module (PCM) 406 thattriggers relevant actions in light of the events detected by DLC 408. Inorder words, PCM 406 is the decision maker, subject to policy. Forexample, PCM 406 may employ rules that control when DLA 400 is to sendinformation to the SCA (e.g., alerts, predictions, recommended actions,trending data, etc.) and/or modify a network behavior itself. Forexample, PCM 406 may determine that a particular traffic flow should beblocked (e.g., based on the assessment of the flow by TIP 404 and DLC408) and an alert sent to the SCA.

Network Control Component (NCC) 418 is a module configured to triggerany of the actions determined by PCM 406 in the network nodes associatedwith DLA 400. In various embodiments, NCC 418 may communicate thecorresponding instructions 422 to the network nodes using APIs 420(e.g., DQoS interfaces, ABR interfaces, DCAC interfaces, etc.). Forexample, NCC 418 may send mitigation instructions 422 to one or morenodes that instruct the receives to reroute certain anomalous traffic,perform traffic shaping, drop or otherwise “black hole” the traffic, ortake other mitigation steps. In some embodiments, NCC 418 may also beconfigured to cause redirection of the traffic to a “honeypot” devicefor forensic analysis. Such actions may be user-controlled, in somecases, through the use of policy maps and other configurations. Notethat NCC 418 may be accessible via a very flexible interface allowing acoordinated set of sophisticated actions. In further embodiments, API(s)420 of NCC 418 may also gather/receive certain network data 424 from thedeployed nodes such as Cisco™ OnePK information or the like.

The various components of DLA 400 may be executed within a container, insome embodiments, that receives the various data records and otherinformation directly from the host router or other networking device.Doing so prevents these records from consuming additional bandwidth inthe external network. This is a major advantage of such a distributedsystem over centralized approaches that require sending large amount oftraffic records. Furthermore, the above mechanisms afford DLA 400additional insight into other information such as control plane packetand local network states that are only available on premise. Note alsothat the components shown in FIG. 4 may have a low footprint, both interms of memory and CPU. More specifically, DLA 400 may use lightweighttechniques to compute features, identify and classify observation data,and perform other functions locally without significantly impacting thefunctions of the host router or other networking device.

As noted above, anomaly detection systems such as SLNs monitor thecommunications between hosts on a network and flag those that exhibitanomalous behaviors (e.g., statistical outliers). To that end, thesystem may collect traffic records 426 which may be the communicationsthemselves, or representations thereof, and analyze the records using ananomaly detector (e.g., DLC 408). Thus, traffic records 426 may includeinformation about the hosts participating in any given communication.

In some cases, identifying whether a party in a communication is aninternal or external host (e.g., with respect to the branch ororganization to which the host belongs) may help to calibrate theanomaly detector. In particular, such information allows the anomalydetector to determine whether all of the communications for a given hostare observed. In addition, the anomaly detector may use the hostinformation to cluster hosts for purposes of analysis. For example, theanomaly detector may aggregate the communications associated with acluster of host devices, to determine whether or not the behavior of thecluster is anomalous.

Note that distinguishing between an “internal network” and an “externalnetwork” may be difficult from a networking point of view, in somesituations. However, most network topologies follow classical patternsallowing for this distinction to be made. In general, a host may beconsidered part of an internal network when a user of the anomalydetection system can intervene on the host, either directly orindirectly (e.g., through escalation within the organization, etc.),when an anomaly is detected. In some cases, the user of the anomalydetection system may manually specify whether a given host is internalor external. However, doing so is both prone to errors and cumbersome toupdate.

Learning Internal Ranges from Network Traffic Data to Augment AnomalyDetection Systems

The techniques herein allow an anomaly detection system, such as an SLN,to determine whether a given host is considered internal or external. Insome aspects, the techniques leverage insights into the nature ofobserved traffic to determine ranges of network addresses that areinternal or external. In some aspects, the techniques collect trafficrecords that indicate the source/destination address pairs of a networkcommunication. In another aspect, the techniques use these records tosolve an optimization problem (e.g., to find the lowest N-number ofaddress groups/intervals that verify the constraints of the problem, theN-number of address groups that minimize the sum of the intervaldiameters, etc.). In a further aspect, the techniques herein provide thesolution of the optimization problem to the anomaly detection system foruse when detecting anomalies. These functions can be implemented in manydifferent ways, such as on separate physical devices, as differentprocesses, or as sub-processes of the same process, in variousembodiments. Additionally, these functions can also be implementeddirectly as part of the anomaly detection system, in one embodiment.

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a device in a network receives trafficrecords indicative of network traffic between different sets of hostaddress pairs. The device identifies one or more address groupingconstraints for the sets of host address pairs. The device determinesaddress groups for the host addresses in the sets of host address pairsbased on the one or more address grouping constraints. The deviceprovides an indication of the address groups to an anomaly detector.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with the SLNprocess 248, which may include computer executable instructions executedby the processor 220 (or independent processor of interfaces 210) toperform functions relating to the techniques described herein, e.g., inconjunction with routing process 244.

Operationally, FIG. 5 illustrates an example 500 of traffic beingconveyed in a network, according to some embodiments. As shown, assumethat DLA 400 is a border router located on the edge of a network branchand connects the various hosts in the branch to other external networkssuch as the Internet, other branches of an entity's, etc. For purposesof illustration only, such external hosts are represented as belongingto an external network 504 that is outside of the local branch. Further,assume that the branch comprises three separate internal networks 502a-502 c, each of which may have any number of host devices.

Various types of traffic may be present in the network configurationshown in FIG. 5. For example, some of the overall network traffic mayinclude traffic 512 that is conveyed between hosts in internal network502 a and those in the external network 504, in one or both directions.Similarly, the hosts in internal networks 502 b-502 c may exchangetraffic 514-516, respectively, with the hosts in external network 504.

Traffic in the example 500 shown can also be internal traffic betweenthe hosts of internal networks 502 a-502 c, such as intra-branchtraffic. In some cases, this traffic may still flow through DLA 400(e.g., the branch router, etc.). For example, the hosts in internalnetworks 502 a and 502 c may exchange traffic 510 with one another viaDLA 400. However, in other cases, traffic 506 between the hosts ofinternal networks 502 a-502 b and traffic 508 between the hosts ofinternal networks 502 b-502 c may not traverse DLA 400 (e.g., traffic506-508 may be conveyed via other networking devices deeper in thebranch, etc.).

While the example 500 in FIG. 5 illustrates one possible scenario, it isto be appreciated that DLA 400 may be any form of networking device(e.g., router, switch, etc.) connected to N-number of internal networks,denoted I₁ to I_(N). Further, DLA 400 may be connected to the externalworld (e.g., external network 504) via a LAN or WAN interface.

Thus, in many cases, the following assumptions can be made about theanomaly detection system deployed in the network:

-   -   1. The anomaly detection system (e.g., DLA 400) does not observe        traffic between two external hosts in external network 504.    -   2. The anomaly detection system does not observe traffic from an        internal network to itself (e.g., from one host in internal        network 502 a to another host also in internal network 502 a,        etc.).    -   3. The internal networks I₁ to I_(N) can be represented as        groups/intervals of contiguous network addresses. Such intervals        can be of any non-zero size.

By way of example, the following address groups/intervals may representthe internal networks 502 a-502 c shown in FIG. 5:

-   -   Internal network 502 a: {10.2.3.0, . . . , 10.2.3.25},    -   Internal network 502 b: {123.1.1.0, . . . , 123.1.1.255},    -   Internal network 502 c: {145.56.78.90}.        In other words, internal network 502 a may use a total of twenty        six IP addresses, internal network 502 b may use a total of 256        IP addresses, and network 502 c may use only a single IP        address. As used herein, the number of network addresses in a        given address group/interval is referred to as the “diameter” of        the corresponding network.

Also based on the above assumptions about the traffic observed by theanomaly detection system (e.g., DLA 400), whenever the anomaly detectionsystem observes traffic sent from a host A to a host B, it means thateither 1.) A or B is in external network 504 or 2.) that both A and Bare in internal networks 502, but belong to different internal networks(e.g., host A belongs to internal network 502 a and host B belongs tointernal network 502 c). As a consequence, each traffic recordindicating that host A has communicated with a host B conveysinformation about the location of the hosts. However, additionalanalysis is required to pinpoint the locations of hosts A-B, as thereare two possibilities for any given host address pair in terms of theirlocations.

Referring now to FIG. 6, an example address grouping process 600 isshown, according to various embodiments. In general, address groupingprocess 600 may be configured to group network addresses based on hostaddress pairs in traffic records 416. In some embodiments, addressgrouping process 600 may be executed by the DLA (e.g., as a sub-process416 of DLA 400, etc.) or another device in communication therewith.

Traffic records 416 can be collected in any number of different wayssuch as via Netflow, IPFIX, via direct observation by the host device(e.g., a border router, etc.). As shown, traffic records 416, atminimum, may indicate the pairs of host addresses involved in a givencommunication (e.g., the source and destination addresses). When addressgrouping process 600 is implemented directly on a DLA 400, it may sharethe collection mechanism used by the anomaly detector. For example, NSC416, an interface, etc. may collect traffic records 416 for processingby address grouping process 600. Otherwise, if address grouping process600 is implemented on another device, it can connect to the networkelement to capture this information via Netflow, packet capture, or thelike. Regardless of the capture mechanism used, the system may maketraffic records 416 available to address grouping process 600, such asvia a shared memory, remote procedure calls (RPCs), internal procedurecalls (IPCs), or the like.

In some embodiments, address grouping process 600 may determine theaddress groups/intervals for the host addresses in traffic records 416by solving a constrained optimization problem. Notably, the aboveexpressions regarding the possible locations of a host participating ina conversation may serve as the constraints for such a problem. In turn,address grouping process 600 may attempt to find the smallest number ofinternal address ranges that verify the constraints. Further, addressgrouping process 600 may do so at any time, such as when traffic data416 is updated, after expiration of a timer, in response to an explicitrequest to do so, etc.

In general, the constrained problem to be solved by address groupingprocess 600 may be formulated as follows:

For N from 1 to N_(max), find the N-number of internal address rangeintervals I₁ to I_(N) such that all source/destination address pairs (A,B) in traffic data 416 satisfy one of the following:

-   -   A belongs to E and B belongs to I_(n), for some n in {1 . . .        N}, where E is the group of external addresses.    -   B belongs to E and A belongs to I_(n), for some n in {1 . . .        N}, where E is the set of external ranges.    -   A belongs to I_(n) and B belongs to I_(m), for some n in {1 . .        . N} and m in {1 . . . N}, with n being different from m.

When formulated in such a way, it is clear that a solution exists with Pdistinct address groups/intervals, where P is the number of uniqueaddresses in traffic records 416. Said differently, one solution wouldbe to place each address in its own address group with an internaldiameter of 1. However, this is the trivial solution. What is of greaterinterest is the solution that optimally distributes the host addressesto the groups while satisfying the above.

Given N, address grouping process 600 may compute the address groups ina number of different ways. In some embodiments, address groupingprocess 600 may use a satisfiability modulo theories (SMT) solver usinglinear arithmetic, to determine the optimal address groups. In general,SMT solvers work to solve a set of input logic expressed as a set ofinequalities. Example SMT solvers include Absolver, CVC3/CVC4, veriT,although any SMT solver may be used.

To formulate the optimization problem for an SMT solver, the constraintscan be rewritten in inequality form:

-   -   A belongs to I_(n), which is an [inf I_(n), sup I_(n)] interval,        iff inf I_(n)≤A≤sup I_(n)    -   A belongs to E iff A does not belong to any L. In other words, A        belongs to E if, for any n such that 1≤n≤N, A>sup I_(n) or A<inf        I_(n).        For unicity purposes, it is assumed that for n<m, sup I_(n)<inf        I_(n). The objective function is then the sum over n of sup        I_(n)−inf I_(n).

Address grouping process 600 can terminate the processing in severalways:

-   -   1. Address grouping process 600 can succeed in finding N≤N_(max)        ranges that verify the constraints, in which case the intervals        are made available to the anomaly detection mechanism.    -   2. Address grouping process 600 can fail to find fewer than N        ranges that verify the constraints, in which case a failure flag        is exposed (e.g., to a user interface, to the anomaly detection        system, etc.).    -   3. The computation of the solution by address grouping process        600 exceeds the resources allocated (e.g., in terms of CPU,        memory, etc.), in which case a failure flag is exposed (e.g., to        a user interface, to the anomaly detection system, etc.).

In an alternative formulation, the problem can be left unsolved for aproportion of the hosts, to be handled by the anomaly detector as itsees fit.

A further variation of the techniques herein assumes that the hostdevices that belong to the same subrange also belong to the same range.Thus, instead of address grouping process 600 simply assessing the(source address, destination address) pair, it may instead operate onthe (source subrange, destination address) pair. In the case of an IPv4address, a suitable subrange can be a /30, or /28. This greatly reducesthe number of constraints for address grouping process 600, speeding upthe computation.

Typically, address grouping process 600 may be executed at the edge of anetwork (e.g., of a branch office, etc.). Hence, Address groupingprocess 600 may label all IP addresses external to the branch as“external,” whether they belong to the enterprise network or not.

As shown in FIGS. 7A-7C other implementations provide for differentnetworking devices in the enterprise network to collaborate, todetermine the host address groups. In some embodiments, the devices maycommunicate with one another, to refine their own classifications. Bybroadcasting to one another the groups/ranges that they classified asinternal, the different devices can work to refine the classifications.For example, as shown in FIG. 4A, DLA 400 may send its own sets ofaddress groups 702 to any number of DLAs 400 a-400 n (e.g., a firstthrough nth DLA).

In turn, when a device receives the address groups from another, it mayperform any or all of the following:

-   -   Confirm the classification they inferred and solve any “unknown”        cases. Notably, if a range is unknown in a branch and internal        in another, it becomes external in the first one.    -   Discriminate between corporate and non-corporate ranges. A range        that is external for all devices is non-corporate. This assumes        however that all the branches of the corporate network implement        the techniques herein.    -   Additionally, a given device may also specify whether it knows        with a high probability that a given host address is external.        For example, if one of DLAs 400 a-400 n runs an exterior gateway        protocol (EGP) such as the Border Gateway Protocol (BGP), it may        be able to determine that a given host address is external by        comparing the address with its own autonomous system (AS).

As shown in FIG. 7B, the confirming devices may then share their results704 with the originator and/or with one another, thereby leveraging theknowledge of the different devices in the enterprise network to groupthe host addresses.

Finally, a more complex variant solves the problem jointly for allnetwork devices, in some embodiments. For example, as shown in FIG. 7C,the various DLAs 400 a-400 n may instead send their traffic records viamessages 706 for address grouping to a central device that executesaddress grouping process 600, such as SCA 700 or another centralizeddevice. In another embodiment, address grouping process 600 isdistributed amongst all of DLAs 400 a-400 n.

If the address groupings are computed jointly, the constraints foraddress grouping process 600 may be modified such that the group ofexternal addresses E now represents only the non-enterprise ranges. Inparticular, the optimization problem may be rewritten as follows:

For all network devices R₁ to R_(K), and for N from 1 to N_(max), findthe K times N ranges I₁ ¹ to I_(N) ^(K) such that for all host addresspairs (A,B), either:

-   -   A belongs to E and B belongs to I_(n) ^(K), for some n in {1 . .        . N} and k in {1 . . . K}    -   B belongs to E and A belongs to I_(n) ^(K), for some n in {1 . .        . N} and k in {1 . . . K}    -   A belongs to I_(n) ^(K) and B belongs to I_(m) ^(K), for some k        in {1 . . . K}, n in {1 . . . N}, and m in and m in {1 . . . N},        where n is different than m    -   A belongs to I_(n) ^(K) and B belongs to I_(m) ^(j), for some        kin {1 . . . K}, j in {1 . . . K}, n in {1 . . . N} and m in {1        . . . N}, with j different from k and n not necessarily        different from m.

Regardless of how address grouping process 600 determines the addressgroups, a further aspect of the techniques herein provides for process600 to send the results to the anomaly detection system, as shown inFIG. 6. Address grouping process 600 may do so using any number ofdifferent mechanisms such as shared memory, RPCs, IPCs, function callswithin the same process, or the like. In turn, the anomaly detectionsystem may use the distinctions between internal and external hostaddresses for purposes of anomaly detection. For example, if a set ofhosts are located in the same internal address group, the anomalydetection system may treat the host devices as a cluster and assesstheir behaviors as a group.

Preliminary testing has shown the techniques herein to be highlyaccurate. Indeed, the core assumption that is made is both reasonableand verified (i.e., that whenever two hosts are seen exchanging trafficby the system these hosts either belong to different internal groups, orone of them is external).

A first experiment using the techniques herein was conducted usingsynthetic data. This synthetic data was formed by first defining a setof internal groups, then by sampling hosts from these internal group, orfrom the external group, and by sampling pairs of hosts that couldpotentially communicate with one another. On this data, the techniqueswere able to accurately recover the internal groups that were defined inthe input data set. However, while this experiment gave a positiveresult, this experiment only tests the correct transcription of theproblem as constraints and the correct functioning of the SMT solver.

A second experiment was also performed to test the techniques herein onactual network traffic. In particular, traffic records were gatheredfrom a branch office of an enterprise network over the span of fiftydays. From this, approximately, 24,000 unique pairs of IPv4 addresseswere observed during the collection time period. To verify the resultsof the techniques, the operator of the network also supplied a list ofthree IPv4 ranges belonging to the branch. As the actual list ofaddresses is considered sensitive, the addresses are partially redactedherein by using letters to represent an undisclosed number between 0 and255: 10.A.B.0/24 (group 1), 10.A.C.0/24 (group 2), and 10.D.E.192/26(group 3).

From the experiment, the following address groups/ranges were obtained:10.D.E.192/26, 10.A.B.0/25, 10.A.B.128/25, 10.A.C.0/24, F.G.106.0/26,F.H.128.128/26, and I.64.0.0/21. Of first note is that the system wasable to correctly identify the three address groups supplied by theoperator of the network, with only the second group split into two /25ranges. This split is likely attributable to a first host in the first/25 exchanging traffic with another host in the second /25.

Interestingly, the techniques also found three ranges (F.G.106.0/26,F.H.128.128/26, I.64.0.0/21) that belong to the public, routable IPv4address space. WHOIS queries revealed them to belong to the sameoperator of the network which were probably headquarters ranges,internal to the organization, but external to the branch, and which canbe considered as such by the system.

These experiments show that the techniques herein can yield reasonableinternal ranges lists, which could also be fine-tuned via user input(e.g., by tagging, or merging, or adjusting ranges), if need be. This ismuch more efficient than having the user manually supply the entire listoutright, which may be prone to errors and cumbersome to update.

FIG. 8 illustrates an example simplified procedure for providing addressgroups to an anomaly detector in a network in accordance with one ormore embodiments described herein. For example, a non-generic,specifically configured device (e.g., device 200) may perform procedure800 by executing stored instructions (e.g., process 248). The procedure800 may start at step 805, and continues to step 810, where, asdescribed in greater detail above, the device may receive trafficrecords regarding traffic in the network. Generally, such trafficrecords may indicate the host address pairs (e.g., source anddestination) of traffic observed in the network either by the deviceitself or another device in communication therewith.

At step 815, as detailed above, the device may identify one or moreaddress grouping constraints for the sets of host address pairs. Forexample, a constraint may be that any given communication is between aninternal and external network, between an external address and aninternal network, or between two internal networks. In some embodiments,these constraints may be codified as inequalities as part of aconstrained optimization problem.

At step 820, the device may determine address groups for the hostaddresses in the traffic based on the one or more address groupingconstraints, as described in greater detail above. In some embodiments,the device may use a satisfiability modulo theories (SMT) solver on theaddresses, in view of the constraint(s), to determine the addressgroups. Such address groups may include, for example, a group ofaddresses that are deemed external to the network and one or more groupsof addresses that are deemed internal to the network. For example, theremay be multiple internal groups of addresses at a given branch.

At step 825, as detailed above, the device may provide an indication ofthe determined address groups to an anomaly detector. In turn, theanomaly detector may use this information to better assess the behaviorsof the host devices. For example, the anomaly detector may form acluster of hosts that belong to the same address group, under theassumption that the hosts are expected to exhibit similar behavior. Theanomaly detector may also use the address groupings in other ways forpurposes of anomaly detection, as well. Procedure 800 then ends at step830.

It should be noted that while certain steps within procedure 800 may beoptional as described above, the steps shown in FIG. 8 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, allow for the grouping ofhost addresses and to distinguish hosts that are “internal” from thosethat are “external” to the network. Such information may be leveraged byan anomaly detection system in the network, such as an SLN.

While there have been shown and described illustrative embodiments thatprovide for the learning of internal vs. external host addressranges/groups, it is to be understood that various other adaptations andmodifications may be made within the spirit and scope of the embodimentsherein. For example, while certain embodiments are described herein withrespect to using certain models for purposes of anomaly detection, themodels are not limited as such and may be used for other functions, inother embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: receiving, at a device in anetwork, traffic records indicative of network traffic between differentsets of host address pairs; identifying, by the device, one or moreaddress grouping constraints for the sets of host address pairs;determining, by the device, address groups for the host addresses in thesets of host address pairs based on the one or more address groupingconstraints; and providing, by the device, an indication of the addressgroups to an anomaly detector.
 2. The method as in claim 1, wherein aparticular one of the one or more address groups comprises hostaddresses associated with an external network that is external to thenetwork.
 3. The method as in claim 2, wherein a second one of theaddress groups comprises host addresses associated with an internalnetwork that is internal to the network.
 4. The method as in claim 1,wherein determining the address groups for the host addresses in thesets of host pairs comprises: using, by the device, a satisfiabilitymodulo theories (SMT) solver on the identified one or more addressgrouping constraints for the sets of host address pairs.
 5. The methodas in claim 4, wherein the one or more address grouping constraintsindicates that a particular host address pair must be associated withone of: traffic sent from an internal network to an external network,traffic sent from an external network to an internal network, or trafficbetween two internal networks.
 6. The method as in claim 1, wherein thedevice is a supervisory device and the traffic records are received froma plurality of distributed learning agents configured to execute anomalydetectors.
 7. The method as in claim 1, further comprising: providing,by the device, the address groups to one or more other devices in thenetwork; and receiving, by the device, confirmation of the addressgroups from the one or more other devices in the network.
 8. The methodas in claim 1, further comprising: receiving, at the device, addressgroups from a second device in the network; comparing, by the device,the determined address groups to the received address groups; andproviding, by the device, a result of the comparison to the seconddevice in the network.
 9. The method as in claim 1, wherein the deviceis a border router and hosts the anomaly detector.
 10. The method as inclaim 1, wherein the anomaly detector uses the address groups to analyzetraffic for host clusters that are based on the address groups.
 11. Anapparatus, comprising: one or more network interfaces to communicatewith a network; a processor coupled to the network interfaces andconfigured to execute one or more processes; and a memory configured tostore a process executable by the processor, the process when executedoperable to: receive traffic records indicative of network trafficbetween different sets of host address pairs; identify one or moreaddress grouping constraints for the sets of host address pairs;determine address groups for the host addresses in the sets of hostaddress pairs based on the one or more address grouping constraints; andprovide an indication of the address groups to an anomaly detector. 12.The apparatus as in claim 11, wherein a particular one of the one ormore address groups comprises host addresses associated with an externalnetwork that is external to the network, and wherein a second one of theaddress groups comprises host addresses associated with an internalnetwork that is internal to the network.
 13. The apparatus as in claim11, wherein the apparatus determines the address groups for the hostaddresses in the sets of host pairs by using a satisfiability modulotheories (SMT) solver on the identified one or more address groupingconstraints for the sets of host address pairs.
 14. The apparatus as inclaim 13, wherein the one or more address grouping constraints indicatesthat a particular host address pair must be associated with one of:traffic sent from an internal network to an external network, trafficsent from an external network to an internal network, or traffic betweentwo internal networks.
 15. The apparatus as in claim 11, wherein theapparatus is a supervisory device and the traffic records are receivedfrom a plurality of distributed learning agents configured to executeanomaly detectors.
 16. The apparatus as in claim 11, wherein the processwhen executed is further operable to: provide the address groups to oneor more other devices in the network; and receive confirmation of theaddress groups from the one or more other devices in the network. 17.The apparatus as in claim 11, wherein the process when executed isfurther operable to: receive address groups from another device in thenetwork; compare the determined address groups to the received addressgroups; and provide a result of the comparison to the other device inthe network.
 18. The apparatus as in claim 11, wherein the apparatus isa border router and hosts the anomaly detector.
 19. The apparatus as inclaim 11, wherein the anomaly detector uses the address groups toanalyze traffic for host clusters that are based on the address groups.20. A tangible, non-transitory, computer-readable medium storing programinstructions that cause a device in a network to execute a processcomprising: receiving, at the device, traffic records indicative ofnetwork traffic between different sets of host address pairs;identifying, by the device, one or more address grouping constraints forthe sets of host address pairs; determining, by the device, addressgroups for the host addresses in the sets of host address pairs based onthe one or more address grouping constraints; and providing, by thedevice, an indication of the address groups to an anomaly detector.